In networked data technologies, the introduction and expanded use of middleware platforms has enabled a wider range of applications access to a wider range of data sources. Middleware platforms in general are known which allow a single application to access diverse or incompatible data sources, by commonizing the data schema used by those separate sources for use by the application.
In the realm of data analysis, packages for statistical analysis are known which are configured to receive data in standardized formats. For example, online analytical processing (OLAP) applications in general manipulate data which is stored in a multi-dimensional format, and manipulate multi-dimensional data to generate reports, statistics, and other outputs. Middleware platforms which attempt to combine multiple data sources for purposes of feeding data to one or more OLAP or other statistical applications can encounter difficulties, however. Those issues include the fact that the diverse data sources will typically not be built with completely common or consistent formats or dimensions. A statistical application which wishes to attempt, for example, to generate an average of data that is merged from multiple sources may find it impossible to locate the desired field in each separate source, and generate an average on the combined set of data. It may be desirable to provide methods and systems for the generation of multi-population statistical measures via middleware, in which a middleware aggregation engine generates a combined data object from multiple data sources, and permits statistical applications to generate reports or other outputs based on the combined data object.